This package provides a list of rviz2 plugins for human-related data visualisation. It is part of the ROS4HRI ecosystem.

A plugin for visualising 2D information overlayed on a camera stream (ideally, the stream used to detect it). Currently, the plugin can visualise:

• Face bounding boxes
• Face landmarks
• Body bounding boxes
• 2D skeleton keypoints

0. If not already available, add a Displays panel;
1. press the Add button at the bottom to istantiate a new plugin;
2. select By topic;
3. among the available topics, select the Humans plugin for the camera stream you are interested in;
4. once created the plugin, select which type of information you want to visualise (face bounging boxes, skeleton landmarks, etc.);
5. enjoy!

A plugin for visualising the estimated 3D poses of the detected humans.

0. If not already available, add a Displays panel;
1. press the Add button at the bottom to istantiate a new plugin;
2. select By display type;
3. select Skeletons3D;
4. enjoy!

A plugin for visualising the human-related TF frames. These are higly dynamical, appearing and disappearing in a matter of seconds. Using the classic TF plugin would result in a crowded and chaotic frames visualisation. This plugin:

• looks over the detected faces and bodies;
• only displays the face and bodies TF frames for the currently detected bodies and faces;
• readily remove the TF frames for those bodies and faces that are no more tracked, avoiding the disappearing phase observed in the original TF frame for the non-updated frames. It is possible to select which human frames to visualise among:
• face frames;
• gaze frames;
• body frames;

0. If not already available, add a Displays panel;
1. press the Add button at the bottom to istantiate a new plugin;
2. select By display type;
3. select TF_HRI;
4. enjoy!

To test the hri_rviz plugins:

1. Download hri_face_detect and hri_fullbody;
2. build them;
3. start an RGB camera stream;
4. start face and body detection:
   • ros2 launch hri_face_detect face_detect.launch.py filtering_frame:=<camera_frame> rgb_camera:=<rgb_camera_stream_ns>
   • ros2 launch hri_fullbody hri_fullbody.launch.py rgb_camera:=<rgb_camera_stream_ns> where <rgb_camera_stream_ns> is the RGB camera stream namespace (e.g., /camera/color). This expects the raw RGB images to be published on <rgb_camera_stream_ns>/image_raw. Check the launch files parameters for different options.
5. start the plugins as previously described.